We use the INTERACTION dataset [1]} to train our predictors. We use segments of the data of 40 timesteps from the beginning, middle, and end of runs (10 timesteps as the history, and the other 30 timesteps as the label). We train a LSTM model for our end-to-end predictor with pooling layers to encourage the interactions among agents in a similar way as social-LSTM [2]} (see Appendix B for details), and we use Inverse Reinforcement Learning (IRL) to recover a cost function for our planning-based predictor using features for collision avoidance, progress, lane keeping, etc., similar to [3]}, [4]}. To enable the model to predict collision avoidance with other agents, we use the cost function to first optimize predictions for further away agents, and iteratively compute trajectories for nearby agents until we reach the target agent. To avoid counfounding effects from the need to predict the geometric intent of agents, we provide both predictors with the overall reference path the agent is following (e.g. which exit they are taking in a roundabout). Further details are in Appendix A.
